What’s New in Microsoft Databases: Empowering AI-Driven App Development

Session Date: May 19, 2025
Duration: 1 hour
Venue: Build 2025 Conference - BRK204
Speakers: Arun Ulag (CVP Azure Data Team, Microsoft), Shireesh Thota (CVP Azure Databases, Microsoft), Priya Sathy (Partner Director of Product SQL, Microsoft), Charles Feddersen (Partner Director of Program Management, Microsoft), Kirill Gavrylyuk (VP Azure Cosmos DB, Microsoft), Genis Campa (Head of Data Innovation, NTT Data)
Link: Microsoft Build 2025 Session BRK204

Microsoft Database Portfolio

Table of Contents

  1. Introduction to Microsoft’s AI-First Database Strategy
  2. SQL Server 2025: The AI-Ready Enterprise Database
  3. Azure SQL Database Evolution
  4. PostgreSQL: Open Source AI Integration
  5. Cosmos DB: Global AI Database Platform
  6. Live Demonstrations and Real-World Applications
  7. Customer Success Stories
  8. Technical Architecture and Integration Patterns

Introduction to Microsoft’s AI-First Database Strategy

00:00:00 - 00:05:30 (5m 30s)
Speakers: Arun Ulag

Microsoft’s comprehensive approach to AI-ready databases centers on the fundamental principle that “AI is only as good as the data that it gets to work on.” This session establishes the critical importance of data quality in AI success, introducing Microsoft’s most comprehensive database portfolio across the industry.

The Data-AI Imperative

The session opens with Arun Ulag emphasizing the “garbage in, garbage out” principle that governs AI implementations. Organizations must prioritize getting their data AI-ready as the foundational step for successful AI initiatives.

Core Strategic Elements:

  • Data Quality Foundation: AI model success directly correlates with data quality and preparation
  • Comprehensive Portfolio: Microsoft offers the industry’s most complete database offering
  • Three Decades of SQL Server: Maintaining leadership in enterprise scale, reliability, and security
  • AI Integration Built-In: Every database service includes AI-ready capabilities from inception

Market Leadership Metrics

The presentation establishes Microsoft’s database portfolio momentum through key performance indicators:

Azure SQL Database:

  • Used by 97% of Fortune 500 companies
  • Massive enterprise scale adoption
  • Critical workload handling for largest global organizations

Azure Cosmos DB:

  • #1 database for building AI applications (Bloomberg CIO survey)
  • ChatGPT infrastructure foundation
  • Petabyte-scale customer implementations

PostgreSQL Leadership:

  • Microsoft has more PostgreSQL committers than any other hyperscaler
  • #1 in PostgreSQL 17 commits
  • Active community contribution beyond just hosting

This introduction sets the stage for understanding how Microsoft’s database strategy directly addresses the AI transformation requirements facing modern enterprises.

SQL Server 2025: The AI-Ready Enterprise Database

00:05:30 - 00:15:45 (10m 15s)
Speakers: Arun Ulag, Shireesh Thota

Public Preview Launch and Adoption

The major announcement of SQL Server 2025 Public Preview represents a significant milestone in enterprise database evolution, demonstrating unprecedented customer enthusiasm with 2x the adoption rate compared to SQL Server 2022.

Adoption Metrics and Customer Response:

  • 2x Adoption Rate: Double the early adoption compared to previous SQL Server versions
  • Customer Validation: Extensive private preview feedback from Intel, Mediterranean Shipping, Hewlett Packard, and AMD
  • AI Feature Demand: AI capabilities identified as the most requested and widely used features
  • Enterprise Ready: Battle-tested technology foundation with three decades of reliability

Built-in Vector Search Capabilities

SQL Server 2025 introduces native AI capabilities that eliminate the need for external vector databases, providing enterprise-grade AI functionality directly within the familiar SQL Server environment.

Technical AI Integration Features:

  • Native Vector Data Type: Binary format embedding support for efficient storage and retrieval
  • DiskANN Vector Indexing: Microsoft Research technology powering Bing search integrated into SQL Server
  • Multi-Language Model Support: Seamless switching between local Ollama and Azure OpenAI models
  • Semantic Search Integration: Natural language query capabilities embedded in enterprise database

Live Demonstration Highlights: The session includes a compelling live demonstration showcasing:

  • Multilingual Product Search: English and Chinese product catalogs searched simultaneously
  • Dynamic Model Switching: Real-time transitions between local and cloud AI models
  • Semantic Kernel Integration: Direct C# application development with AI capabilities
  • Automatic Code Generation: Vector store operations generated automatically

Security and Integration Enhancements

Enterprise security and hybrid cloud integration represent critical enhancements in SQL Server 2025, addressing modern organizational requirements for secure, distributed AI implementations.

Enterprise Security Framework:

  • Microsoft Entra Managed Identity: Replacing API key authentication for Azure resource access
  • ARC Enablement: Hybrid cloud scenarios with consistent security policies
  • Secure AI Model Connections: Enterprise-grade authentication for AI service integration

Fabric Integration Capabilities:

  • OneLake Connectivity: Data available in open Delta Parquet format across data estate
  • Mirroring Capabilities: Real-time data synchronization with analytics platforms
  • Cross-Cloud Availability: Deployment flexibility across any cloud or on-premises environment

The integration demonstrates Microsoft’s commitment to providing enterprise customers with AI-ready capabilities while maintaining the security, reliability, and performance standards expected from SQL Server.

Azure SQL Database Evolution

00:15:45 - 00:25:30 (9m 45s)
Speakers: Priya Sathy, Charles Feddersen

SQL Server Management Studio 21 GA

The general availability of SQL Server Management Studio 21 represents a complete modernization of the primary database management tool, incorporating contemporary development practices and AI-powered capabilities.

Modernization Achievements:

  • Visual Studio 2022 Shell: Modern development environment foundation
  • 64-bit Architecture Support: Enhanced performance and memory handling
  • Dark Mode and Modern UI: Contemporary user experience design
  • Git Integration: Version control for database development workflows
  • Enhanced Query Editor: Improved results grid and query execution experience
  • Azure Authentication Built-in: Seamless cloud service integration

SSMS 21 Copilot Integration

The public preview of Copilot integration within SSMS 21 introduces AI-powered database administration and development capabilities directly within the management environment.

AI-Powered Database Management Features:

  • Natural Language Query Generation: Write, edit, and tune queries using plain English descriptions
  • Context-Aware Responses: AI understands actual database schema and data relationships
  • Database Administration Assistance: Configuration, maintenance, and troubleshooting guidance
  • Intelligent Performance Recommendations: AI-driven optimization suggestions based on workload analysis

Developer Experience Enhancement: The integration extends beyond query generation to include:

  • Schema Exploration: AI-assisted database structure understanding
  • Query Optimization: Performance tuning recommendations with execution plan analysis
  • Error Resolution: Contextual debugging assistance for database issues

Azure SQL Hyperscale Innovations

Azure SQL Hyperscale continues to push the boundaries of transactional database scale and performance, with new capabilities addressing the most demanding enterprise workloads.

Enterprise Scale Performance Metrics:

  • 30 Read Replicas: Massive read scale-out capabilities
  • 100+ Terabyte Support: Transactional workloads at unprecedented scale
  • 50% Faster than AWS Aurora: Superior price-performance compared to competitors
  • Continuous Priming (GA): Consistent performance during failover operations
  • 150 MB/s Log Throughput: Optimized for write-intensive workloads

Customer Success Case Study - UBS: The session highlights UBS as a premier example of Azure SQL Hyperscale enterprise adoption:

  • 2 Petabytes of Data: Migrated from mainframe infrastructure
  • 50,000 Tables: Comprehensive database schema management
  • 400 Billion Records: Massive data volume processing
  • Tier 1 Banking Reliability: Mission-critical financial services workloads

Developer Experience Enhancements:

VS Code Integration:

  • MSSQL Extension with Copilot: AI-powered query generation within IDE
  • ORM Migration Support: Automated database schema evolution
  • Database Chat Functionality: Natural language database interaction for application development

JSON Data Support Innovation:

  • JSON Indexing (Public Preview): High-performance queries on semi-structured data
  • Native JSON Types: Optimized binary storage format
  • JSON Aggregation: Seamless relational-to-JSON data transformations
  • 2GB Document Capacity: Large document storage within relational fields

The evolution of Azure SQL Database demonstrates Microsoft’s commitment to providing developers and database administrators with modern, AI-enhanced tools while maintaining enterprise-grade reliability and performance.

PostgreSQL: Open Source AI Integration

00:25:30 - 00:35:15 (9m 45s)
Speakers: Shireesh Thota, Charles Feddersen

Vector Search Performance Breakthrough

Azure Database for PostgreSQL introduces revolutionary vector search capabilities that dramatically improve AI application performance through Microsoft’s DiskANN technology integration.

DiskANN for PostgreSQL (GA) Performance Metrics:

  • 35 Million Vectors: Queried in under 1 second response time
  • 10x Performance Improvement: HNSW comparison showing 1000ms → 100ms average latency reduction
  • Product Quantization Optimization: Advanced compression techniques for efficient vector storage
  • Superior Accuracy: Enhanced precision compared to traditional pgvector implementations

Technical Architecture Advantages: The DiskANN integration provides enterprise-scale vector search capabilities that surpass traditional PostgreSQL vector extensions, enabling organizations to handle massive AI workloads with consistent sub-second response times.

Semantic Operators Innovation

The public preview of semantic operators represents a groundbreaking advancement in natural language SQL integration, allowing developers to write AI-powered queries using familiar SQL syntax.

Four Semantic Operators Introduction:

GENERATE Operator:

  • ChatGPT-style Content Generation: Within SQL query context
  • Dynamic Content Creation: Based on existing data relationships
  • Multi-model Support: Integration with various language models

IS_TRUE Operator:

  • Semantic Predicate Evaluation: Natural language condition assessment
  • Context-Aware Filtering: Understanding of conceptual relationships
  • Boolean Logic Integration: Seamless integration with traditional SQL WHERE clauses

EXTRACT Operator:

  • Entity Knowledge Extraction: Automatic identification of relevant information
  • Structured Data Generation: From unstructured text sources
  • Pattern Recognition: AI-powered data parsing and categorization

RANKING Operator:

  • AI-Powered Result Relevance: Intelligent ordering of query results
  • Context-Sensitive Scoring: Understanding of query intent and data relationships
  • Multi-criteria Optimization: Balancing multiple relevance factors

Practical Implementation Example:

SELECT product_name, reviews 
FROM products 
WHERE reviews IS_TRUE 'positive sentiment about comfort'
ORDER BY reviews RANKING 'best for gaming'

This example demonstrates how semantic operators enable natural language concepts to be directly embedded within SQL queries, bridging the gap between AI capabilities and traditional database operations.

High Availability Architecture

PostgreSQL’s enhanced availability features address enterprise requirements for minimal downtime and consistent performance across critical workloads.

Premium SSD v2 Integration (Public Preview):

  • Sub-10 Second Failover: Rapid recovery from infrastructure issues
  • Performance Parity: Maintaining existing SSD solution performance levels
  • Enhanced Reliability: Improved infrastructure resilience for critical workloads

Developer Experience Evolution:

VS Code PostgreSQL Extension Enhancements:

  • Native Entra ID Authentication: Enterprise identity integration
  • Azure-Integrated Experience: Subscription and resource group navigation
  • Copilot Optimization: PostgreSQL-specific query generation and optimization
  • Performance Analysis: Query optimization examples showing 38ms to 8.5ms improvements
  • Export Capabilities: Excel, JSON, CSV data export functionality

Graph Database Capabilities:

Apache AGE Extension (GA):

  • Native Cypher Queries: Graph query language support within PostgreSQL
  • Semantic Relationships: Beyond traditional foreign key constraints
  • Complex Pattern Matching: Advanced graph traversal and analysis
  • Product-Review-Feature Modeling: Real-world graph relationship examples

Customer Success Story - PTC: The session highlights PTC’s successful PostgreSQL implementation:

  • 300 Complex Scenarios: Migrated to Azure Database for PostgreSQL
  • Increased Reliability: Enhanced system stability and performance
  • Cost Efficiency Gains: Resources redirected to AI application development
  • AKS and Semantic Kernel Integration: Kubernetes and AI framework connectivity

The PostgreSQL innovations demonstrate Microsoft’s commitment to open source database excellence while providing enterprise-grade AI capabilities that leverage the flexibility and power of the PostgreSQL ecosystem.

Cosmos DB: Global AI Database Platform

00:35:15 - 00:45:00 (9m 45s)
Speakers: Kirill Gavrylyuk

AI Foundry Native Integration

Azure Cosmos DB’s integration with AI Foundry represents a strategic advancement in building intelligent applications with native thread management and conversational AI capabilities.

Thread Services Integration (Preview):

  • Structured Conversations: Multi-turn interactions between users and AI agents
  • Context Preservation: Maintaining conversation state across application sessions
  • Bring Your Own Storage: Custom thread management with Cosmos DB as backend
  • Foundry SDK Embedding: Direct integration for intelligent application development

Architectural Benefits: The AI Foundry integration enables developers to build sophisticated conversational AI applications with enterprise-grade data persistence, global distribution, and automatic scaling capabilities inherent to Cosmos DB.

Performance and Scale Innovations

Cosmos DB continues to push the boundaries of global database performance through innovations in indexing, search capabilities, and query optimization.

Global Secondary Indexing Innovation:

  • Read-Only Containers: Alternative partition key strategies for query optimization
  • Cross-Partition Query Optimization: Improved performance for non-partition-key queries
  • Performance Boost: Significant query response time improvements
  • Cost Reduction: Targeted indexing strategies reducing unnecessary computational overhead

Vector and Full-Text Search (GA):

  • Hybrid Search Capabilities: Combining vector similarity with BM25 text ranking algorithms
  • Multi-Language Support (Public Preview): Global application language requirements
  • Fuzzy Search with Typo Tolerance: Enhanced user experience for search applications
  • Phrase Search Capabilities: Complex text pattern matching and retrieval

Zero-Downtime Architecture

Cosmos DB introduces revolutionary availability guarantees through per-partition automatic failover capabilities, achieving unprecedented uptime commitments.

Per-Partition Automatic Failover Architecture:

  • Surgical Failover: Individual partition recovery without full database disruption
  • Zero Recovery Time (RTO = 0): Instant recovery guarantee
  • Zero Data Loss (RPO = 0): Strong consistency with no data loss commitment
  • Zero Touch Operation: SDK-managed transparent failover without manual intervention

The “000 Database” Concept: This innovative approach delivers:

  • RTO: 0 - No recovery time objective
  • RPO: 0 - No recovery point objective
  • Manual Intervention: 0 - Fully automated operations

Fleet Management for Multi-Tenancy:

SaaS Optimization Features:

  • Shared Throughput: Aggregated resource allocation across tenant accounts
  • Aggregated Monitoring: Centralized management and observability
  • Cost Optimization: Efficient resource utilization for multi-tenant applications
  • Security Isolation: Individual tenant security boundary maintenance

Customer Success Story - NFL: The National Football League’s AI coaching assistant demonstrates Cosmos DB’s real-world AI application capabilities:

  • AI Coaching Assistant: Built entirely on Cosmos DB infrastructure
  • Azure OpenAI Integration: Seamless AI model connectivity for talent evaluation
  • Real-Time Insights: Instant athlete assessment and coaching recommendations
  • Container Services Orchestration: Kubernetes integration for scalable AI workloads

MongoDB API Enhancements:

Open Source Collaboration Initiative:

  • Cosmos Mongo vCore API: Open-sourced for community contribution
  • Partnership with Yugabyte and FerretDB: Collaborative open source development
  • PostgreSQL-Backed Document API: Leveraging PostgreSQL strengths for document storage
  • Community Contribution: Active participation in document database ecosystem evolution

Enterprise MongoDB Features:

  • Entra ID Authentication: Enterprise identity integration for MongoDB vCore clusters
  • DiskANN Vector Search: High-performance vector capabilities for document databases
  • Transactional Semantics: ACID guarantees with document database flexibility

Cosmos DB’s evolution demonstrates Microsoft’s commitment to providing a globally distributed, AI-ready NoSQL platform that combines the flexibility of document databases with enterprise-grade reliability and AI integration capabilities.

Live Demonstrations and Real-World Applications

00:45:00 - 00:55:30 (10m 30s)
Speakers: Priya Sathy, Charles Feddersen, Kirill Gavrylyuk

SQL Server 2025 AI Application Development

The live demonstration of SQL Server 2025 showcases the practical implementation of AI-ready database capabilities in real-world application development scenarios.

Multi-Language Vector Search Implementation:

  • Bilingual Product Catalog: English and Chinese product search within single database instance
  • Dynamic Model Switching: Runtime transitions between local Ollama and Azure OpenAI models
  • Semantic Kernel Integration: Direct C# application development with minimal configuration
  • Automatic Code Generation: Vector store operations automatically generated for developers

Technical Implementation Highlights: The demonstration reveals how SQL Server 2025 eliminates traditional barriers between database operations and AI capabilities:

  • Native Vector Storage: Embeddings stored directly in SQL Server without external dependencies
  • Seamless Model Integration: AI models accessed through familiar SQL syntax and procedures
  • Performance Optimization: Query execution optimized for vector similarity operations
  • Developer Productivity: Reduced complexity in building AI-powered applications

Azure SQL Hyperscale Insurance Application

The comprehensive insurance application demonstration showcases advanced Azure SQL capabilities integrated with modern development frameworks and AI-powered user interfaces.

Data API Builder (DAB) Integration:

  • REST and GraphQL Endpoints: Automatic API generation from database schema
  • Claims-Based Security: User context and authorization integrated at database level
  • JSON Document Storage: Up to 2GB per field for complex structured data
  • Model Context Protocol (MCP) Server: Integration enabling natural language database interaction

Advanced Business Logic Scenarios: The demonstration includes complex multi-step operations triggered through natural language chat interface:

  • Customer Name Changes: Multi-table updates with referential integrity maintenance
  • Address Updates: Geographic data validation and normalization
  • Insurance Policy Addition: Complex business rule enforcement and notification systems
  • Email Integration: Automated notification systems triggered by database changes

Chat-Driven Database Operations: The application demonstrates revolutionary user interaction patterns:

  • Natural Language Commands: Complex database operations expressed in conversational language
  • Multi-Step Transaction Management: Atomic operations across multiple tables and business rules
  • Real-Time Validation: Business rule enforcement with immediate user feedback
  • Audit Trail Integration: Comprehensive change tracking for regulatory compliance

PostgreSQL Vector Search Performance

The PostgreSQL demonstration focuses on extreme-scale vector search performance and the practical application of semantic operators in real-world scenarios.

35 Million Vector Performance Demonstration:

  • Sub-Second Response Times: Consistent performance with massive vector datasets
  • DiskANN Technology Showcase: Microsoft Research technology integrated into PostgreSQL
  • Semantic Operator Re-ranking: Result optimization through AI-powered relevance scoring
  • Graph Query Integration: Apache AGE extension demonstrating complex relationship queries

Query Optimization Live Examples: The demonstration includes real-time query optimization showcasing:

  • Performance Analysis: 38ms to 8.5ms latency reduction through Copilot suggestions
  • Semantic Operator Usage: Natural language concepts embedded in SQL queries
  • Multi-Model Integration: Various AI models accessed through consistent PostgreSQL interface
  • Graph Relationship Queries: Complex pattern matching across product-review-feature graphs

Developer Workflow Integration: The PostgreSQL demonstration emphasizes developer experience improvements:

  • VS Code Extension Capabilities: Seamless development environment integration
  • Azure Authentication: Native Entra ID integration for enterprise security
  • Export Functionality: Data visualization through Excel, JSON, and CSV formats
  • Performance Monitoring: Real-time query analysis and optimization recommendations

Cosmos DB Agentic Applications

The Cosmos DB demonstration showcases advanced AI agent architecture with multi-tenant chat platforms and intelligent tool selection capabilities.

Multi-Tenant Chat Platform Architecture:

  • MCP Client-Server Framework: Model Context Protocol implementation for agent communication
  • Per-Tenant Container Isolation: Security and data isolation for multi-tenant scenarios
  • Semantic Caching: Vector similarity-based response optimization
  • Hybrid Search Integration: Automatic tool selection based on query characteristics
  • AI Foundry Portal Integration: Seamless agent deployment and management

Advanced Agent Capabilities: The demonstration reveals sophisticated AI agent behaviors:

  • Context-Aware Responses: Understanding of conversation history and user intent
  • Intelligent Tool Selection: Automatic choice between search methods based on query type
  • Multi-Turn Conversation Management: State preservation across extended interactions
  • Dynamic Response Optimization: Performance tuning based on query patterns and user feedback

The live demonstrations collectively illustrate Microsoft’s database portfolio’s readiness for AI-first application development, showing how traditional database operations seamlessly integrate with modern AI capabilities while maintaining enterprise-grade performance, security, and reliability standards.

Customer Success Stories

00:55:30 - 01:00:00 (4m 30s)
Speakers: Arun Ulag, Shireesh Thota, Genis Campa

Enterprise Transformations

The session highlights several major enterprise customers who have successfully transformed their data infrastructure using Microsoft’s database portfolio, demonstrating real-world AI integration at scale.

UBS (Banking Sector): UBS represents one of the most significant enterprise database transformations in the financial services industry:

  • Mainframe Migration Scale: 2 petabytes of data migrated from legacy mainframe systems
  • Data Complexity: 50,000 tables with 400 billion records managed
  • Mission-Critical Reliability: Tier 1 banking operations requiring 99.99% uptime
  • Azure SQL Hyperscale Implementation: Demonstrating enterprise-scale transactional workload capabilities

Technical Achievement Significance: The UBS migration demonstrates Azure SQL Hyperscale’s capability to handle the world’s most demanding financial workloads while providing the reliability and security standards required by global banking institutions.

BMW (Automotive Industry): BMW’s implementation showcases AI integration in automotive data processing:

  • Mobile Data Recorder Infrastructure: Vehicle data collection and processing systems
  • PostgreSQL Flexible Server: Foundation for conversation and chat history management
  • AI-Powered Capabilities: Real-time vehicle data analysis and insights
  • Scalable Architecture: Supporting global automotive data requirements

NFL (Sports Technology): The National Football League’s AI coaching assistant represents innovative AI application development:

  • Azure OpenAI Integration: Sophisticated talent evaluation and coaching guidance
  • Cosmos DB Foundation: Globally distributed data platform for real-time insights
  • Container Services Orchestration: Kubernetes integration for scalable AI workloads
  • Real-Time Analytics: Instant athlete assessment during field activities

Industry-Specific Implementations

PTC (Industrial Technology): PTC’s transformation demonstrates PostgreSQL’s capabilities in complex industrial scenarios:

  • 300 Complex Scenarios: Comprehensive migration to Azure Database for PostgreSQL
  • Reliability Improvements: Enhanced system stability and performance consistency
  • Cost Efficiency Achievement: Resources redirected toward AI application development
  • AKS and Semantic Kernel Integration: Advanced Kubernetes and AI framework connectivity

NTT Data Partnership: Genis Campa from NTT Data provides perspective on Microsoft database adoption across enterprise clients:

  • Global System Integration: Large-scale database modernization projects
  • AI-Ready Infrastructure: Preparing enterprise clients for AI transformation
  • Multi-Industry Experience: Database solutions across various industry verticals
  • Partnership Value: Microsoft database portfolio enabling comprehensive digital transformation

Mediterranean Shipping Company: As one of the SQL Server 2025 private preview participants:

  • Global Logistics Operations: Worldwide shipping and logistics data management
  • AI Integration Requirements: Advanced analytics for supply chain optimization
  • Enterprise Scale Testing: Validation of SQL Server 2025 capabilities at global scale

Intel Corporation: Intel’s participation in SQL Server 2025 private preview:

  • Semiconductor Industry Requirements: High-performance computing and data analysis
  • AI Model Development: Advanced AI capabilities for chip design and manufacturing
  • Performance Validation: Testing SQL Server 2025 AI features at enterprise scale

Hewlett Packard Enterprise: HPE’s involvement in private preview validation:

  • Enterprise Technology Integration: Comprehensive database portfolio evaluation
  • AI Infrastructure Development: Supporting AI-ready enterprise solutions
  • Performance Benchmarking: Validating database capabilities for enterprise customers

These customer success stories demonstrate Microsoft’s database portfolio’s capability to handle diverse industry requirements while providing consistent AI-ready capabilities, enterprise-grade reliability, and global scale performance across various sectors from financial services to automotive, sports technology, and industrial applications.

Technical Architecture and Integration Patterns

00:15:00 - 00:45:00 (Distributed throughout session)
Speakers: All presenters

AI-Ready Database Architecture

Microsoft’s database portfolio demonstrates a comprehensive AI-first architecture that integrates AI capabilities directly into database engines rather than requiring external AI infrastructure.

Native AI Integration Principles:

  • Embedded Vector Storage: AI embeddings stored natively within database structures
  • Integrated AI Models: Direct model access through database query languages
  • Semantic Query Processing: Natural language concepts embedded in SQL operations
  • Real-Time AI Operations: AI processing integrated with transactional workloads

Cross-Database AI Capabilities:

SQL Server 2025 AI Architecture:

  • DiskANN Vector Indexing: Microsoft Research technology for high-performance vector search
  • Multi-Model Support: Seamless integration between local Ollama and Azure OpenAI models
  • Semantic Kernel Integration: C# framework integration for AI application development
  • Native Vector Data Types: Binary format optimization for embedding storage and retrieval

PostgreSQL AI Integration:

  • Semantic Operators Framework: Four operator types (GENERATE, IS_TRUE, EXTRACT, RANKING)
  • DiskANN Performance: 35 million vector queries under 1 second response time
  • Apache AGE Graph Extension: Cypher query language support within PostgreSQL
  • Multi-Language AI Model Support: Various AI model integrations through consistent interface

Cosmos DB AI Platform:

  • AI Foundry Integration: Native thread services for conversational AI applications
  • Hybrid Search Capabilities: Vector similarity combined with BM25 text ranking
  • Global AI Distribution: AI operations distributed across global Cosmos DB regions
  • Multi-Tenant AI Isolation: Per-tenant AI model and data isolation

Cross-Platform Integration Strategies

Microsoft’s database portfolio demonstrates sophisticated integration patterns that enable AI capabilities across hybrid cloud, multi-cloud, and on-premises environments.

Fabric Integration Architecture:

  • OneLake Connectivity: All database data available in open Delta Parquet format
  • Mirroring Capabilities: Real-time synchronization between operational databases and analytics platforms
  • Unified Data Estate: Single interface for data across multiple database technologies
  • SaaS Data Platform: Reducing complexity through pre-integrated data services

Azure Resource Connectivity (ARC):

  • Hybrid Cloud AI: Azure AI services attached to data regardless of location
  • On-Premises Integration: Consistent AI capabilities across cloud and on-premises deployments
  • Multi-Cloud Support: Database portfolio available across various cloud platforms
  • Unified Management: Single management plane for distributed database deployments

Security and Identity Integration:

  • Microsoft Entra Managed Identity: Consistent authentication across all database services
  • API Key Replacement: Enhanced security through managed identity authentication
  • Cross-Service Authorization: Unified security model across database and AI services
  • Enterprise Policy Enforcement: Consistent security policies across hybrid environments

Developer Experience Unification:

VS Code Integration Pattern:

  • Consistent Extensions: Similar development experience across SQL Server, PostgreSQL, and other databases
  • Copilot Integration: AI-powered development assistance across all database technologies
  • Database Chat Functionality: Natural language interaction with databases through IDE
  • Cross-Database Query Optimization: Performance tuning assistance across database types

Model Context Protocol (MCP) Implementation:

  • Standardized AI Communication: Consistent interface for AI model integration
  • Database-Agnostic AI Access: Uniform AI capabilities regardless of underlying database
  • Multi-Model Support: Various AI models accessible through single protocol
  • Enterprise Integration: Enterprise-grade AI model management and deployment

Performance and Scaling Patterns:

Auto-Scaling AI Workloads:

  • Dynamic Resource Allocation: Automatic scaling based on AI workload demands
  • Cross-Region AI Distribution: AI processing distributed across geographic regions
  • Load Balancing: AI operations balanced across multiple database instances
  • Cost Optimization: AI resource utilization optimized for cost efficiency

Data Movement and Synchronization:

  • Real-Time AI Data Pipelines: Continuous data flow for AI model training and inference
  • Change Data Capture: AI models updated based on database changes
  • Event-Driven AI Processing: AI operations triggered by database events
  • Batch AI Processing: Scheduled AI operations for large-scale data processing

The technical architecture demonstrates Microsoft’s strategic approach to creating an integrated, AI-ready data platform that maintains consistency across various database technologies while providing enterprise-grade performance, security, and reliability for AI-powered applications.

References

Microsoft Documentation and Official Resources

Microsoft Build 2025 Official Sessions
The official Microsoft Build conference session catalog containing all technical presentations, including this database portfolio overview. Essential for accessing complete session recordings and supplementary materials.

SQL Server 2025 Public Preview Documentation
Comprehensive technical documentation for SQL Server 2025 features, including AI capabilities, vector search implementation, and migration guides. Critical resource for implementing SQL Server AI features in production environments.

Azure SQL Database Documentation
Complete reference for Azure SQL Database services, Hyperscale architecture, and JSON indexing capabilities. Essential for understanding Azure SQL’s AI-ready features and implementation patterns.

Azure Database for PostgreSQL Documentation
Official documentation for PostgreSQL semantic operators, DiskANN integration, and Apache AGE graph extension. Vital for implementing advanced AI features in PostgreSQL environments.

Azure Cosmos DB Developer Guide
Comprehensive guide to Cosmos DB AI capabilities, vector search, and AI Foundry integration. Essential reference for building globally distributed AI applications.

Technical Research and White Papers

DiskANN: Fast Accurate Billion-point Nearest Neighbor Search
Microsoft Research paper detailing DiskANN algorithm implementation, which powers vector search capabilities in SQL Server 2025 and PostgreSQL. Understanding this research provides insight into the technical foundations of Microsoft’s vector search performance achievements.

Apache AGE (A Graph Extension) for PostgreSQL
Official Apache AGE project documentation for graph database capabilities within PostgreSQL. Important for understanding graph query capabilities and Cypher language integration demonstrated in the session.

Model Context Protocol (MCP) Specification
Technical specification for Model Context Protocol used in database-AI integration. Critical for understanding how AI models communicate with database systems in Microsoft’s architecture.

Industry Analysis and Benchmarks

Bloomberg CIO Survey on Database AI Applications
Industry survey results showing Cosmos DB as the leading database for AI application development. Provides market context for Microsoft’s database portfolio positioning in the AI era.

Gartner Cloud Database Management Systems Magic Quadrant
Independent analysis of cloud database providers, including Microsoft’s position in the market. Relevant for understanding competitive landscape and Microsoft’s strategic positioning.

Forrester Wave: Cloud Database Services
Comprehensive evaluation of cloud database services across various criteria. Useful for understanding enterprise database selection criteria and Microsoft’s competitive advantages.

Development Tools and SDKs

Semantic Kernel Framework Documentation
Microsoft’s AI framework for integrating language models with conventional programming. Essential for developers implementing AI applications with Microsoft databases, as demonstrated in SQL Server 2025 scenarios.

Visual Studio Code Database Extensions
Collection of database extensions for VS Code, including MSSQL and PostgreSQL extensions with Copilot integration. Important for developers seeking to implement the development workflow demonstrated in the session.

Azure Data API Builder
Open-source project for generating REST and GraphQL APIs from database schemas. Critical tool for implementing the insurance application architecture demonstrated with Azure SQL Database.

Customer Case Studies and Implementation Guides

UBS Digital Transformation with Azure
Detailed case study of UBS’s migration from mainframe to Azure SQL Hyperscale. Provides real-world implementation insights for large-scale enterprise database modernization.

NFL AI Applications on Azure
Case study of NFL’s AI coaching assistant built on Cosmos DB and Azure OpenAI. Demonstrates practical AI application architecture using Microsoft’s database portfolio.

Enterprise Database Migration Best Practices
Azure Architecture Center guidance for large-scale database migrations. Essential reference for organizations planning database modernization projects similar to those highlighted in the session.

Open Source Projects and Community Resources

PostgreSQL Community Documentation
Official PostgreSQL documentation including vector extensions and performance optimization. Important for understanding the open-source foundation underlying Microsoft’s PostgreSQL offerings.

FerretDB Project
Open-source MongoDB-compatible database built on PostgreSQL. Relevant to Microsoft’s announcement of open-sourcing Cosmos Mongo vCore API and collaboration with the document database community.

Ollama Local AI Model Platform
Platform for running large language models locally, as demonstrated in SQL Server 2025’s multi-model support. Essential for understanding local AI model integration patterns.

Each reference provides specific value for different aspects of implementing Microsoft’s database portfolio for AI applications, from technical implementation details to strategic planning and competitive analysis.

Appendix

A. Technical Specifications and Performance Metrics

SQL Server 2025 AI Capabilities Technical Details:

  • Vector Data Type: Binary format with optimized storage compression
  • DiskANN Index Parameters: Configurable parameters for accuracy vs. performance tradeoffs
  • Model Integration APIs: Technical specifications for AI model connectivity
  • Security Integration: Detailed authentication and authorization mechanisms

Azure SQL Hyperscale Performance Benchmarks:

  • Detailed Performance Comparisons: Comprehensive benchmarks vs. AWS Aurora and Google Cloud SQL
  • Scaling Characteristics: Performance curves for various workload patterns
  • Cost Analysis: TCO calculations for different deployment scenarios
  • Regional Availability: Service availability across Azure regions

PostgreSQL Semantic Operators Implementation:

  • Operator Syntax Reference: Complete SQL syntax for each semantic operator
  • Performance Characteristics: Latency and throughput metrics for various query patterns
  • Model Compatibility Matrix: Supported AI models and their capabilities
  • Configuration Parameters: Tuning parameters for optimal performance

B. Migration and Implementation Guidance

SQL Server to SQL Server 2025 Upgrade Process:

  • Compatibility Assessment: Tools and procedures for evaluating upgrade readiness
  • Migration Strategies: Step-by-step upgrade procedures and rollback plans
  • Feature Adoption: Phased approach to implementing AI capabilities
  • Performance Testing: Validation procedures for upgraded systems

Legacy Database Modernization Patterns:

  • Mainframe Migration Strategies: Detailed approaches for mainframe-to-cloud migrations
  • Oracle to PostgreSQL Migration: Tools and techniques for Oracle database modernization
  • NoSQL to Cosmos DB Migration: Patterns for transitioning from other NoSQL platforms
  • Hybrid Architecture Patterns: Maintaining on-premises and cloud database integration

C. Development Framework Integration Details

Semantic Kernel Integration Patterns:

  • Configuration Examples: Complete code samples for various scenarios
  • Error Handling: Robust error handling patterns for AI-database integration
  • Performance Optimization: Best practices for optimizing AI-database operations
  • Testing Strategies: Unit and integration testing approaches for AI-enabled applications

Model Context Protocol Implementation:

  • Server Setup: Detailed MCP server configuration procedures
  • Client Integration: Application patterns for MCP client implementation
  • Security Configuration: Authentication and authorization for MCP communications
  • Monitoring and Diagnostics: Observability patterns for MCP-based applications

D. Enterprise Architecture Considerations

Multi-Tenant Application Patterns:

  • Data Isolation Strategies: Various approaches to tenant data separation
  • Security Models: Authentication and authorization patterns for multi-tenant systems
  • Scaling Patterns: Resource allocation and performance optimization for SaaS applications
  • Compliance Frameworks: Regulatory compliance patterns for different industries

Disaster Recovery and Business Continuity:

  • Backup Strategies: Comprehensive backup and recovery procedures for AI-enabled databases
  • Failover Patterns: High availability configurations for various database services
  • Data Replication: Cross-region replication strategies for global applications
  • Recovery Testing: Procedures for validating disaster recovery capabilities

E. Security and Compliance Framework

Enterprise Security Implementation:

  • Identity Integration: Detailed Microsoft Entra integration procedures
  • Network Security: VPN, private endpoints, and network isolation configurations
  • Encryption Strategies: Data encryption at rest and in transit implementation
  • Audit and Compliance: Logging and monitoring for regulatory compliance

AI Model Security Considerations:

  • Model Access Control: Securing AI model access and usage
  • Data Privacy: Protecting sensitive data in AI processing workflows
  • Prompt Injection Protection: Security measures for natural language interfaces
  • AI Model Governance: Policies and procedures for AI model management

F. Cost Optimization and Resource Management

Database Service Cost Models:

  • Pricing Calculators: Detailed cost estimation for various scenarios
  • Resource Optimization: Strategies for optimizing database resource utilization
  • Reserved Capacity: Long-term cost optimization through reserved pricing
  • Hybrid Cost Management: Balancing on-premises and cloud costs

AI Workload Cost Optimization:

  • Model Usage Optimization: Strategies for efficient AI model utilization
  • Compute Resource Management: Optimizing compute resources for AI workloads
  • Storage Optimization: Efficient vector and AI data storage strategies
  • Monitoring and Alerting: Cost monitoring and budget management procedures

This appendix provides comprehensive technical details, implementation guidance, and operational considerations that complement the main session content, enabling organizations to successfully implement Microsoft’s database portfolio for AI-ready applications while maintaining enterprise standards for performance, security, and cost efficiency.